Search Results (624)

Background: Computed tomography urography (CTU) is routinely used to evaluate the upper urinary tract in patients with hematuria. CTU may detect incidental findings outside the urinary tract, but it remains unclear if this adds value. This study aimed to develop a deep learning algorithm that automatically segments and selectively visualizes the urinary tract on CTU. Methods: The urinary tract (kidneys, ureters, and urinary bladder) was manually segmented on 2 mm dual-phase CTU slices of 111 subjects. With this dataset, a deep learning-based AI was trained to automatically segment and selectively visualize the urinary tract on CTU scans (including accompanying unenhanced CT scans), which we dub “focused view CTU”. Focused view CTU was technically optimized and tested in 39 subjects with hematuria. Results: The technically optimized focused view CTU algorithm provided complete visualization of 97.4% of kidneys, 80.8% of ureters, and 94.9% of urinary bladders. All urinary tract organs were completely visualized in 66.6% of cases. In these cases (excluding 33.3% of cases with incomplete visualization), focused view CTU intrinsically achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 100.0%, 92.3%, 92.9%, and 100.0% for lesions in the urinary tract compared to unmodified CT, although interrater agreement was moderate (κ = 0.528). All incidental findings were successfully hidden by focused view CTU. Conclusions: Focused view CTU provides adequate urinary tract segmentation in most cases, but further research is needed to optimize the technique (segmentation does not succeed in about one-third of cases). It offers selective urinary tract visualization, potentially aiding in assessing relevance and cost-effectiveness of detecting incidental findings in hematuria patients through a prospective randomized trial. Full article

Adhesive capsulitis is a painful and progressive condition marked by significant limitations in shoulder mobility, particularly affecting external rotation. Although magnetic resonance imaging is regarded as the reference standard for assessing intra-articular structures, its high cost and limited availability present challenges in routine clinical use. In contrast, musculoskeletal ultrasound has emerged as an accessible, real-time, and cost-effective imaging modality for both the diagnosis and treatment guidance of adhesive capsulitis. This narrative review compiles and illustrates current evidence regarding the role of ultrasound, encompassing static B-mode imaging, dynamic motion analysis, contrast-enhanced techniques, and sonoelastography. Key sonographic features—such as thickening of the coracohumeral ligament, fibrosis in the axillary recess, and abnormal tendon kinematics—have been consistently associated with adhesive capsulitis and demonstrate favorable diagnostic performance. Advanced methods like contrast-enhanced ultrasound and elastography provide additional functional insights (enabling evaluation of capsular stiffness and vascular changes) which may aid in disease staging and prediction of treatment response. Despite these advantages, the clinical utility of ultrasound remains subject to operator expertise and technical variability. Limited visualization of intra-articular structures and the absence of standardized scanning protocols continue to pose challenges. Nevertheless, ongoing advances in its technology and utility standardization hold promise for the broader application of ultrasound in clinical practice. With continued research and validation, ultrasound is positioned to play an increasingly central role in the comprehensive assessment and management of adhesive capsulitis. Full article

The conductors of high-speed railway OCSs (Overhead Contact Systems) are susceptible to conductor galloping due to the impact of natural elements such as strong winds, rain, and snow, resulting in conductor fatigue damage and significantly compromising train operational safety. Consequently, monitoring the galloping status of conductors is crucial, and instance segmentation techniques, by delineating the pixel-level contours of each conductor, can significantly aid in the identification and study of galloping phenomena. This work expands upon the YOLO11-seg model and introduces an instance segmentation approach for galloping video and image sensor data of OCS conductors. The algorithm, designed for the stripe-like distribution of OCS conductors in the data, employs four-direction Sobel filters to extract edge features in horizontal, vertical, and diagonal orientations. These features are subsequently integrated with the original convolutional branch to form the FDSE (Four Direction Sobel Enhancement) module. It integrates the ECA (Efficient Channel Attention) mechanism for the adaptive augmentation of conductor characteristics and utilizes the FL (Focal Loss) function to mitigate the class-imbalance issue between positive and negative samples, hence enhancing the model’s sensitivity to conductors. Consequently, segmentation outcomes from neighboring frames are utilized, and mask-difference analysis is performed to autonomously detect conductor galloping locations, emphasizing their contours for the clear depiction of galloping characteristics. Experimental results demonstrate that the enhanced YOLO11-seg model achieves 85.38% precision, 77.30% recall, 84.25% AP@0.5, 81.14% F1-score, and a real-time processing speed of 44.78 FPS. When combined with the galloping visualization module, it can issue real-time alerts of conductor galloping anomalies, providing robust technical support for railway OCS safety monitoring. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

(1) Background: A severe decline in knee joint function significantly affects the mobility of the elderly, making it a key concern in the field of geriatric health. To alleviate the pressure on the knee joints of the elderly during daily movements such as sitting and standing, effective biomechanical solutions are required. (2) Methods: In this study, a biomechanical framework was established based on mechanical analysis to derive the transfer relationship between the ground reaction force and the knee joint moment. Experiments were designed to collect knee joint data on the elderly during the sit-to-stand process. Meanwhile, magnetic resonance imaging (MRI) images were processed through a medical imaging control system to construct a detailed digital 3D knee joint model. A finite element analysis was used to verify the model to ensure the accuracy of its structure and mechanical properties. An improved radial basis function was used to fit the pressure during the entire sit-to-stand conversion process to reduce the computational workload, with an error of less than 5%. In addition, a small-target human key point recognition network was developed to analyze the image sequences captured by the camera. The knee joint angle and the knee joint pressure distribution during the sit-to-stand conversion process were mapped to a three-dimensional interactive platform to form a digital twin system. (3) Results: The system can effectively capture the biomechanical behavior of the knee joint during movement and shows high accuracy in joint angle tracking and structure simulation. (4) Conclusions: This study provides an accurate and comprehensive method for analyzing the biomechanical characteristics of the knee joint during the movement of the elderly, laying a solid foundation for clinical rehabilitation research and the design of assistive devices in the field of rehabilitation medicine. Full article

Porcine rotavirus (PoRV), a primary etiological agent of viral diarrhea in piglets, frequently co-infects with other enteric pathogens, exacerbating disease severity and causing substantial economic losses. Its genetic recombination capability enables cross-species transmission potential, posing public health risks. Globally, twelve G genotypes and thirteen P genotypes have been identified, with G9, G5, G3, and G4 emerging as predominant circulating strains. The limited cross-protective immunity between genotypes compromises vaccine efficacy, necessitating genotype surveillance to guide vaccine development. While conventional molecular assays demonstrate sensitivity, they lack rapid genotyping capacity and face technical limitations. To address this, we developed a novel diagnostic platform integrating reverse transcription recombinase-aided amplification (RT-RAA) with CRISPR–Cas12a. This system employs universal primers for the simultaneous amplification of G4/G5/G9 genotypes in a single reaction, coupled with sequence-specific CRISPR recognition, achieving genotyping within 50 min at 37 °C with 10⁰ copies/μL sensitivity. Clinical validation showed a high concordance with reverse transcription quantitative polymerase chain reaction (RT-qPCR). This advancement provides an efficient tool for rapid viral genotyping, vaccine compatibility evaluation, and optimized epidemic control strategies. Full article

For automatic tumor segmentation in magnetic resonance imaging (MRI), deep learning offers very powerful technical support with significant results. However, the success of supervised learning is strongly dependent on the quantity and accuracy of labeled training data, which is challenging to acquire in MRI. Semi-supervised learning approaches have arisen to tackle this difficulty, yielding comparable brain tumor segmentation outcomes with fewer labeled samples. This literature review explores key semi-supervised learning techniques for medical image segmentation, including pseudo-labeling, consistency regularization, generative adversarial networks, contrastive learning, and holistic methods. We specifically examine the application of these approaches in brain tumor MRI segmentation. Our findings suggest that semi-supervised learning can outperform traditional supervised methods by providing more effective guidance, thereby enhancing the potential for clinical computer-aided diagnosis. This literature review serves as a comprehensive introduction to semi-supervised learning in tumor MRI segmentation, including glioma segmentation, offering valuable insights and a comparative analysis of current methods for researchers in the field. Full article

In music, plagiarism has been an important but troubled issue, which becomes ever more critical with the widespread usage of generative AI tools. Meanwhile, the development of techniques for music similarity detection has been hampered by the scarcity of legally verified data on plagiarism. In this paper, we present a technical solution for training music similarity detection models through the use of comparative imagery data. With the aid of feature-based analysis and data visualization, we conducted experiments to analyze how different music features may contribute to the judgment of plagiarism. While the feature-based analysis guided us to focus on a subset of features, whose similarity is typically associated with music plagiarism, data visualization inspired us to train machine learning models using such comparative imagery instead of using audio signals directly. We trained feature-based sub-models (convolutional neural networks) using imagery data and an ensemble model with Bayesian interpretation for combining the predictions of the sub-models. We tested the trained model with legally verified data as well as AI-generated music, confirming that the models produced with our approach can detect similarity patterns which are typically associated with music plagiarism. Furthermore, using imagery data as the input and output of an ML model has been proven to facilitate explainable AI. Full article

Background/Objectives: Digital prosthodontics increasingly utilize both additive (3D printing) and subtractive Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), yet comprehensive comparisons remain limited. This scoping review evaluates their relative performance across prosthodontic applications. Methods: Systematic searches (PubMed, Scopus, Web of Science, Embase, 2015–2025) identified 28 studies (27 in vitro, 1 retrospective). Data were extracted on accuracy, efficiency, materials, and outcomes. Results: CAD/CAM milling demonstrated superior accuracy for fixed prostheses, with marginal gaps for milled zirconia (123.89 ± 56.89 µm), comparable to optimized 3D-printed interim crowns (123.87 ± 67.42 µm, p = 0.760). For removable prostheses, milled denture bases achieved a trueness of 65 ± 6 µm, while SLA-printed dentures post-processed at 40 °C for 30 min showed the lowest root mean square error (RMSE) (30 min/40 °C group). Three-dimensional printing excelled in material efficiency (<5% waste vs. milling > 30–40%) and complex geometries, such as hollow-pontic fixed dental prostheses (FDPs) (2.0 mm wall thickness reduced gaps by 33%). Build orientation (45° for crowns, 30–45° for veneers) and post-processing protocols significantly influenced accuracy. Milled resins exhibited superior color stability (ΔE00: 1.2 ± 0.3 vs. 3D-printed: 4.5 ± 1.1, p < 0.05), while 3D-printed Co-Cr frameworks (SLM) showed marginal fits of 8.4 ± 3.2 µm, surpassing milling (130.3 ± 13.8 µm). Digital workflows reduced chairside time by 29% (154.31 ± 13.19 min vs. 218.00 ± 20.75 min). All methods met clinical thresholds (<120 µm gaps). Conclusions: Milling remains preferred for high-precision fixed prostheses, while 3D printing offers advantages in material efficiency, complex designs, and removable applications. Critical gaps include long-term clinical data and standardized protocols. Future research should prioritize hybrid workflows, advanced materials, and AI-driven optimization to bridge technical and clinical gaps. Full article

Fluid effects are important in films and advertisements, where their realism and aesthetic quality directly impact the visual experience. With the rapid advancement of digital technology and computer-aided design (CAD), modern visual effects are used to simulate various water-related phenomena, such as flowing water, ocean waves, and raindrops. However, creating these realistic effects is not solely dependent on advanced software and hardware; it also requires an understanding of the technical and artistic aspects of visual effects artists. In the creation process, the artist must possess a keen aesthetic sense and innovative thinking to craft stunning visual effects to overcome technological constraints. Whether depicting the grandeur of turbulent ocean scenes or the romance of gentle rain, the artist needs to transform fluid effects into expressive visual language to enhance emotional impact, aligning with the storyline and the director’s vision. The production process of fluid effects typically involves the following critical steps. First, the visual effects artist utilizes CAD-based tools, particle systems, or fluid simulation software to model the dynamic behavior of water. This process demands a solid foundation in physics and the ability to adjust parameters flexibly according to the specific needs of the scene, ensuring that the fluid motion appears natural and smooth. Next, in the rendering stage, the simulated fluid is transformed into realistic imagery, requiring significant computational power and precise handling of lighting effects. Finally, in the compositing stage, the fluid effects are seamlessly integrated with live-action footage, making the visual effects appear as though they are parts of the actual scene. In this study, the technical details of creating fluid effects using free software such as Blender were explored. How advanced CAD tools are utilized to achieve complex water effects was also elucidated. Additionally, case studies were conducted to illustrate the creative processes involved in visual effects production to understand how to seamlessly blend technology with artistry to create unforgettable visual spectacles. Full article

Three-dimensional food printing (3DFP), a specialized application of additive manufacturing (AM), employs a layer-by-layer deposition process guided by digital image files to fabricate edible structures. Utilizing heavily modified 3D printers and Computer-Aided Design (CAD) software technology allows for the precise creation of customized food items tailored to individual aesthetic preferences and nutritional requirements. Three-dimensional food printing holds significant potential in revolutionizing the food industry by enabling the production of personalized meals, enhancing the sensory dining experience, and addressing specific dietary constraints. Despite these promising applications, 3DFP remains one of the most intricate and technically demanding areas within AM, particularly in the context of modern gastronomy. Challenges such as the rheological behaviour of food materials, print stability, and the integration of cooking functions must be addressed to fully realize its capabilities. This article explores the possibilities of applying classical modified 3D printers in the food industry. The behaviour of certain recipes is also tested. Two test case scenarios are covered. The first scenario is the work and formation of a homogenized meat mass. The second scenario involves finding a chocolate recipe that is suitable for printing relatively detailed chocolate decorative elements. The current advancements, technical challenges, and future opportunities of 3DFP in the field of engineering, culinary innovation and nutritional science are also explored. Full article

Background: While dental implants have become a reliable solution for tooth loss, their long-term success is increasingly challenged by biological and technical complications such as impact fracture and peri-implantitis. These complications significantly impact implant longevity and patient satisfaction. Aim: This consensus conference aimed to identify and standardize clinical guidelines to prevent implant fractures and peri-implant diseases based on current evidence and expert opinions. Methods: A panel of 10 expert clinicians and researchers in prosthodontics participated in the Osstem Global Consensus Meeting. This paper focuses on the prosthetic division. A structured literature review was conducted, and evidence was synthesized to formulate consensus-based clinical recommendations. Participants answered structured questions and discussed discrepancies to achieve consensus. Results: The panel reached consensus on several key prosthetic risk factors, including (1) the role of biomechanical overload in implant fracture, (2) the impact of emergence profile design on peri-implant tissue stability, (3) the influence of implant positioning and connection geometry on marginal bone loss, and (4) the importance of occlusal scheme and restorative material selection, particularly in high-risk patients such as bruxers. Guidelines to prevent implant fracture and peri-implantitis were developed, addressing these factors with practical preventive strategies. Conclusions: Despite the limitations of narrative methodology and reliance on retrospective data and expert opinion, this consensus provides clinically relevant guidelines to aid in the prevention of mechanical failures and peri-implant diseases. The recommendations emphasize prosthetically driven planning, individualized risk assessment, and early intervention to support long-term implant success. Full article

In early 2025, the Santorini–Amorgos area (Aegean Volcanic Arc, Greece) experienced a seismic swarm, with dozens of M ≥ 4.0 earthquakes and a maximum magnitude of M = 5.2. Beyond its seismological interest, the sequence was notable for triggering rare increased preparedness actions by Greek Civil Protection operational structures in anticipation of an imminent destructive earthquake. These actions included (i) risk communication, (ii) the reinforcement of operational structures with additional personnel and equipment on the affected islands, (iii) updates to local emergency plans, (iv) the dissemination of self-protection guidance, (v) the activation of emergency alert systems, and (vi) volunteer mobilization, including first aid and mental health first aid courses. Although it was in line with contingency plans, public participation was limited. Volunteers helped bridge this gap, focusing on vulnerable groups. The implemented actions in Greece are also compared with increased preparedness during the 2024–2025 seismic swarms in Ethiopia, as well as preparedness before the highly anticipated major earthquake in Istanbul (Turkey). In Greece and Turkey, legal and technical frameworks enabled swift institutional responses. In contrast, Ethiopia highlighted the risks of limited preparedness and the need to embed disaster risk reduction in national development strategies. All cases affirm that preparedness, through infrastructure, planning, communication, and community engagement, is vital to reducing earthquake impacts. Full article

This paper explores the optimal configuration strategies for building-integrated photovoltaic (BIPV) systems in response to the low-carbon transformation needs of semi-outdoor substations, aiming to reconcile the contradiction between photovoltaic (PV) power generation efficiency and indoor environmental control in industrial buildings. Taking a 220 kV semi-outdoor substation of the China Southern Power Grid as a case study, a building energy consumption–PV power generation coupling model was established using EnergyPlus software. The impacts of three PV wall constructions and different building orientations on a transformer room and an air-conditioned living space were analyzed. The results show the EPS-filled PV structure offers superior passive thermal performance and cooling energy savings, making it more suitable for substation applications with high thermal loads. Building orientation plays a decisive role in the net energy performance, with an east–west alignment significantly enhancing the PV module’s output and energy efficiency due to better solar exposure. Based on current component costs, electricity prices, and subsidies, the BIPV system demonstrates a moderate annual return, though the relatively long payback period presents a challenge for widespread adoption. East–west orientations offer better returns due to their higher solar exposure. It is recommended to adopt east–west layouts in EPS-filled PV construction to optimize both energy performance and economic performance, while further shortening the payback period through technical and policy support. This study provides an optimized design path for industrial BIPV module integration and aids power infrastructure’s low-carbon shift. Full article

Crop chlorophyll contents affect growth, and accurate assessment aids field management. SPAD (Soil Plant Analysis Development) values of leaves were mainly used to estimate chlorophyll content. Background interference affects the accuracy of SPAD value inversion. To address this issue, a rice leaf SPAD inversion method combining deep learning and feature selection is proposed. First, a leaf segmentation model based on U-Net was established. Then, the color features of leaf images were extracted. Seven color features highly correlated with SPAD were selected via the Pearson correlation coefficient and recursive feature elimination optimization. Finally, leaf SPAD inversion models based on random forest, support vector regression, BPNNs, and XGBoost were established. The results demonstrated that the U-Net could achieve accurate segmentation of leaves with a maximum mean intersection over union (MIoU) of 88.23. The coefficients of determination R² between the anticipated and observed SPAD values of the four models were 0.819, 0.829, 0.896, and 0.721, and the root mean square errors (RMSEs) were 2.223, 2.131, 1.564, and 2.906. Through comparison, the method can accurately predict SPAD in different low-definition and saturation images, showing a certain robustness. It can offer technical support for accurate, nondestructive, and expedited evaluation of crop leaves’ chlorophyll content via machine vision. Full article